Robots are electro-mechanical devices that are able to manipulate objects using a series of robotic links. The robotic links are interconnected by joints, each of which may be independently or interdependently driven by one or more actuators. Each robotic joint represents an independent control variable or degree of freedom. End effectors are end links that directly perform a task such as grasping a work tool or stacking parts. Typically, robots are controlled to a desired target value via closed-loop force, impedance, or position-based control laws.
In manufacturing, there exists a drive for more flexible factories and processes that are able to produce new or more varied products with a minimum amount of downtime. To accomplish this goal, robotic platforms should be able to quickly adapt themselves to new tasks without the need for time consuming reprogramming and compilation. Task demonstration, also referred to as imitation learning, is an evolving approach for achieving such performance flexibility. However, existing task demonstration-based training methods may be less than optimal in terms of the required number of training demonstrations and overall statistical computation workload.